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Matrix Multiplications on GPUs Run Faster When Given “Predictable” Data (2024)

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11–20 of 60 posts

Re: Matrix Multiplications on GPUs Run Faster When Given “Predictable” Data (2024)

#11
I can't tell from the blog, is this actually verified or is it theory and then numbers showing plausibility?

I could certainly come up with alternative theories about memory compression and prefetching if we were talking about texture reads.

Re: Matrix Multiplications on GPUs Run Faster When Given “Predictable” Data (2024)

#12
> For example, when the GPU is fully idle, nvidia-smi tells me that it’s only pulling 88W of power.

I haven't used a non-laptop GPU in some time, but that is a crazy amount of "idle" power consumption. Is this normal for cards like this?

Re: Matrix Multiplications on GPUs Run Faster When Given “Predictable” Data (2024)

#15
post #12

> For example, when the GPU is fully idle, nvidia-smi tells me that it’s only pulling 88W of power. I haven't used a non-laptop GPU in some time, but that is a crazy amount of "idle" power consumption. Is this normal for cards like this?

Server cards are not optimized for idle power usage. They’re expected to be fully utilized.

For server gear it’s more common to have less dynamic power and voltage switching because it produces more predictable performance and latency.

Re: Matrix Multiplications on GPUs Run Faster When Given “Predictable” Data (2024)

#16

Earlier quoted context omitted.

In general, constraints require optimizations and rearchitectures. I'd also expect the ram shortage for instance to have a big impact on the software industry as a whole, specially in games. They will need to make do with what people have, a ps5/pro or similar in PC power.

I actually think it is a good thing to introduce constraints to AI and the overall tech industry. Hopefully everyone will have to look at improving performance without having to add RAM or increase CPU/GPU performance.

As long as these constraints are for everyone and not just for thee and not for me, and become an instrument for big tech to keep consumers dependent on their infra.

Re: Matrix Multiplications on GPUs Run Faster When Given “Predictable” Data (2024)

#17
post #14

It wouldn't surprise me to see some ML algorithm in silico somewhere to select faster matmul paths on favorable data. Yo dawg, I heard you like AI, so we put some AI in your AI so you can infer while you're inferring.

And there's at least one more level of inception at the data center level, where they use AI to optimize power usage (particularly by predictively controlling cooling, and adaptively rescheduling tasks).

Re: Matrix Multiplications on GPUs Run Faster When Given “Predictable” Data (2024)

#18
post #7
post #2

People have been noticing the effects of this in local LLM inference. Power limiting seems to improve overall performance!

This is not observable from LLM inference, where you would not encounter uniform matrices. Power limiting does not improve performance but it does improve efficiency. You might be able to get 90% of the performance for only 70% of the power usage, for example. It does not make the card go faster though.

When thermal throttling occurs you can perform faster by running slower.

This is precicely because of the efficiency. The lower efficiency of the higher speed triggers a much lower performance sooner.

Re: Matrix Multiplications on GPUs Run Faster When Given “Predictable” Data (2024)

#19
post #6

I went in expecting to find 'branch prediction'[0] as the answer, but apparently things are even more complex nowadays. [0] - https://stackoverflow.com/questions/11227809/why-is-conditio...

>I went in expecting to find 'branch prediction'[0]

GPUs do branch prediction? I thought they didn't bother and try to minimize wasted effort by using high amounts of concurrent threads?

Re: Matrix Multiplications on GPUs Run Faster When Given “Predictable” Data (2024)

#20
post #13

Sounds like a side channel attack waiting to happen.

So I guess we'll all be applying a random rotation to our matrices now to obscure their contents, like TurboQuant does. https://arkaung.github.io/interactive-turboquant/#rotation
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